app – Alpholio™https://alpholio.com/blog
BlogMon, 12 Feb 2018 19:06:51 +0000en-UShourly1https://wordpress.org/?v=4.4.1Equal-Weighting S&P 500https://alpholio.com/blog/2015/07/12/equal-weighting-sp-500/
https://alpholio.com/blog/2015/07/12/equal-weighting-sp-500/#respondSun, 12 Jul 2015 23:28:50 +0000https://alpholio.com/blog/?p=1632Read more…]]>The popular market-proxy S&P 500® index is market-cap weighted. This is one of the factors that helps reduce the turnover of ETFs tracking this index. For example, the iShares Core S&P 500 ETF (IVV) has a turnover rate of only 4%. The following chart, produced by the Alpholio™ App for Android, shows the characteristics of a portfolio composed solely of this ETF:

(Note that Alpholio™ uses a broader ETF as a representation of “the market”; hence, the beta of IVV is different from the conventional one and alpha from zero.)

However, market-cap weighting implies that the largest companies’ stocks have the highest impact on the index. While returns of mega-caps in the index tend to be less volatile, they are usually lower than those of their smaller-cap peers. To overcome this limitation, other ETFs weight equities in the index differently. For example, the Guggenheim S&P 500™ Equal Weight ETF (RSP) assigns each of the 500 stocks a 0.2% weight. This tilts RSP toward smaller-cap equities in the index and results in a 18% turnover. Over the same analysis period, RSP produced markedly higher returns than IVV but at the expense of an elevated volatility and a slightly lower Sharpe ratio:

In addition to overweighting of mega-caps, some economic sectors in the index dominate others, as shown in the latest edition of S&P Capital IQ The Outlook:

Sector

Weight %

Consumer Discretionary

12.7

Consumer Staples

9.4

Energy

7.8

Financials

16.5

Health Care

15.3

Industrials

10.2

Information Technology

19.9

Materials

3.2

Telecommunication Services

2.2

Utilities

2.9

To counteract this, the ALPS Equal Sector Weight ETF (EQL) applies the same weight to nine sectors (with telecommunication services considered part of information technology). Here are the characteristics of a portfolio consisting solely of this ETF over the identical analysis period:

While the annualized return of EQL was lower than than of IVV or RSP, it was more than adequately offset by a decrease in volatility, which resulted in an improved Sharpe ratio and maximum drawdown.

What if the investor wanted to equal-weight all ten sectors instead of just nine, i.e. keep telecoms separate from IT? To do so, the investor could construct a portfolio of Vanguard sector ETFs, excluding the Vanguard REIT ETF (VNQ). That is because real estate stocks are currently part of the financials sector and not expected to become a separate asset class until mid-2016. Here is how such a portfolio, rebalanced quarterly (just like EQL), performed over the same analysis period:

The Vanguard sector portfolio had the second highest alpha and Sharpe ratio as well as the second lowest standard deviation (a measure of volatility of returns).

The above analysis period was dictated by the inception date of the EQL, the youngest of all the ETFs used. Arguably, this approximately six-year period may be considered too short and not representative of performance over a full economic cycle. However, it was interesting to see that while equal-weighting the index on a security level produced highest absolute returns, equal-weighting on a sector-level delivered the highest risk-adjusted returns.

To conduct your own analyses of various ETF portfolios, download the Alpholio™ app from

]]>https://alpholio.com/blog/2015/07/12/equal-weighting-sp-500/feed/0Analysis of Henderson Global Equity Income Fundhttps://alpholio.com/blog/2015/05/21/analysis-henderson-global-equity-income-fund/
https://alpholio.com/blog/2015/05/21/analysis-henderson-global-equity-income-fund/#respondThu, 21 May 2015 20:17:34 +0000https://alpholio.com/blog/?p=1598Read more…]]>A recent piece in Barron’s covers the Henderson Global Equity Income Fund (HFQAX; Class A shares). This $3.7 billion fund has a front sales charge of up to 5.75% and a relatively low expense ratio of 1.09%. According to the article

International stocks often pay dividends annually rather than quarterly, allowing the fund’s managers to move in and out of stocks based on the timing of their payouts. That’s how the fund manages a robust 6.03% trailing 12-month yield, even though the average yield among the fund’s holdings is around 2.8%. Of course that also leads to a high turnover rate – at 103% it’s nearly twice the category average. This is a fund best-suited to a tax-advantaged account. The globe-hopping dividend fund has a four-star rating from Morningstar and has outpaced 95% of its peers over the past five years, with an average annual return of 8.33%.

The primary benchmark for the Henderson Global Equity Income fund is the MSCI World Index. One of the accessible implementations of this index is the iShares MSCI World ETF (URTH). Alpholio™’s calculations show that since that ETF’s inception in January 2012, the fund returned more than the ETF in about 18% of all rolling 12-month periods and 6% of rolling 24-month periods. However, this ETF has arguably too short a lifespan to serve as an adequate reference for the fund whose inception date was in November 2006.

The fund’s strategy to capture and pay out infrequent dividends can be emulated from a total return perspective. In the simplest application of Alpholio™’s patented methodology, both the membership and weights of ETFs in the reference portfolio for the analyzed fund are fixed over the entire analysis period. Here are the cumulative RealAlpha™ chart and the related statistics for the fund, generated by the Mutual Fund Service of the Alpolio™ App for Android:

The fund added a miniscule amount of value over the static reference portfolio but did so at the expense of slightly higher volatility (standard deviation of returns).

Here is the reference portfolio for the fund over the same analysis period:

According to the current factsheet, to date the Henderson Global Equity Income Fund

…has provided 100% dividend income and has not returned shareholder capital

Therefore, the article’s statement on the fund’s unsuitability for taxable accounts is somewhat misguided, especially given the current tax treatment of dividends received by moderate income investors. Nevertheless, the above analysis has demonstrated that so far the fund could have been effectively substituted, from a total return perspective, by a fixed portfolio of ETFs. It is also worth noting that the high active share of the fund (over 90%, according to the factsheet) is undoubtedly a result of a frequent equity hopping to sustain its high dividend. This is an example of a strategy whose high active share does not necessarily result in a significant risk-adjusted outperformance.

To learn more about the Henderson Global Equity Income and other mutual funds, please register on our website.

To try the Mutual Fund and other services of the Alpholio™ App for Android, download the app from

]]>https://alpholio.com/blog/2015/05/21/analysis-henderson-global-equity-income-fund/feed/0Growth vs. Valuehttps://alpholio.com/blog/2015/05/04/growth-vs-value/
https://alpholio.com/blog/2015/05/04/growth-vs-value/#respondMon, 04 May 2015 23:44:15 +0000https://alpholio.com/blog/?p=1587Read more…]]>In one of the previous posts, Alpholio™ made the case for increasing the mid-cap stock holdings in the portfolio. As promised, in this follow-on post, we will examine the performance of growth vs. value equities.

Over the past year, the average U.S. large-cap growth fund has risen 18.2%, while the average U.S. large-cap value fund is up 10.4%… from 2003 through 2013, the average gap between the two styles of stock-picking for large-cap stocks was 0.75 percentage point… it’s a similar story among small-company stocks, where growth-stock funds […] are up 16% over the past year. Funds investing in small-cap value stocks […] are up 7.7%.

The trend of growth equities outperforming value equities is hardly a past-year phenomenon. Contrary to what might be expected, this trend is also not confined to the last seven years since the market’s trough during the financial crisis. The trend is best examined using specific ETFs as opposed to hypothetical and unspecified “average U.S. [mutual] funds.”

In that period, the large-cap value ETF handily outperformed its growth counterpart, albeit with a slightly higher standard deviation (a measure of volatility of returns). However, this only paints a part of the picture: in 2000, growth stocks significantly underperformed, following the deflation of the dot-com bubble. If the start of the analysis period is advanced to the beginning of 2001, growth slightly outperformed value:

Through the market peak in October 2007, growth stocks did not advance as much as value ones did, but they suffered a much smaller drawdown (45.4% for growth vs. 56.7% for value, as calculated by the Portfolio service).

The growth outperformance becomes even more pronounced when the beginning of the analysis is moved to April 2005 for a 10-year evaluation period:

Large-cap growth stocks returned about 2% more than their value counterparts, and did so with much smaller volatility. As shown by the Rolling Returns service, in the same period growth outperformed value in about 90% of all rolling 36-month intervals, 67% of 24-month intervals, and 63% of 12-month intervals:

The median difference of rolling 12-month returns over the last 10 years was over 2.6% in favor of growth.

It is worth noting that the outperformance of growth stocks over value ones in this analysis period appears to directly contradict the value effect in the classic three-factor model. However, the latest research from Fama-French indicates that this factor is less important in the presence of the beta, size, profitability and investment factors.

To see all the Alpholio™ App for Android services in action, download the app from

…may offer an attractive way to diversify away from the risks of stocks or bonds …[but] can’t replace bonds, because their returns aren’t certain and come mostly through any price appreciation, not yield. But held in tandem with bonds, they can offer a way to hedge against interest-rate risk and might cushion part of a portfolio against stock-market volatility

The reason why the beta of this portfolio is not exactly 0.6 (i.e. equal to the 60% weight of the SPY) is threefold. Alpholio™ uses a broader definition of “the market” than just the S&P 500® index. Also, the correlation between the market and AGG is not zero. Finally, the portfolio is rebalanced quarterly, not monthly, which can lead to a temporary divergence of SPY/AGG weights from the original 60/40% level.

For reference, in the same time frame a portfolio consisting of just the SPY would have an annualized return of 8.52% with a standard deviation of 14.25%, Sharpe ratio of 0.55 and maximum drawdown of 50.8%. Adding AGG to such an equity-only portfolio decreases its return but reduces its volatility even more, thus improving the Sharpe ratio. The maximum drawdown is also significantly diminished.

The article quotes two merger arbitrage funds with substantial assets: The Merger Fund® (MERFX) and The Arbitrage Fund (ARBFX). To effectively diversify the balanced portfolio, should either fund replace a portion of stocks, a portion of bonds, or a combination of both? What should be the extent of such a replacement?

To answer the first question, let’s take a look at the correlation between SPY, AGG and either fund using the Correlation service of the Alpholio™ app. Here is a chart of the rolling 12-month correlation coefficient for monthly returns of SPY and MERFX:

The starting date of the chart stems from the earliest availability of AGG whose first full monthly return was in October 2003. The average correlation of 0.56 indicates that MERFX was a marginal diversifier for SPY (generally, a correlation of 0.6 or less is desirable). Here is a similar chart for AGG and MERFX:

The average correlation of just below zero indicates that MERFX was a much better diversifier for AGG than SPY. Similarly, the average correlation between SPY and ARBFX was about 0.42 and virtually zero between AGG and ARBFX. Therefore, to effectively diversify the base portfolio, it should generally be better to allocate more of SPY rather than AGG to MERFX or ARBFX. However, this would also suppress portfolio returns — as the following total return chart shows, MERFX and ARBFX had steadier but smaller cumulative returns than SPY:

To answer the second question: a portfolio with the highest Sharpe ratio (i.e. the tangency portfolio) would be mostly composed of AGG and MERFX. Here is an efficient frontier chart in which the current portfolio, depicted by a standalone marker inside the frontier, had 80% in AGG and 20% in MERFX but no SPY and was very close to the tangency portfolio:

Adding MERFX at the expense of SPY decreased the portfolio volatility and increased its Sharpe ratio, but resulted in lower returns. To illustrate this further, here is a chart and statistics for a portfolio that consisted of 45% SPY, 40% AGG and 15% MERFX, rebalanced quarterly:

Ultimately, it is up to the investor to trade off portfolio returns for risk — some may choose to optimize for the highest return per unit of risk, while others may strive for higher returns at the expense of a sub-optimal Sharpe ratio. The Alpholio™ app for Android provides a set of tools that facilitate the exploration of historical data and construction of desired portfolios, with the usual caveat that the past performance is not a guarantee of future results.

]]>https://alpholio.com/blog/2014/11/10/merger-arbitrage-funds-portfolio-diversifiers/feed/0Alpholio™ App for Android – Mutual Fund Servicehttps://alpholio.com/blog/2014/10/24/alpholio-app-android-mutual-fund-service/
https://alpholio.com/blog/2014/10/24/alpholio-app-android-mutual-fund-service/#respondFri, 24 Oct 2014 19:47:46 +0000https://alpholio.com/blog/?p=1480Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the seventh and final one in a series covering the app’s services in more detail.

The Mutual Fund service analyzes mutual fund performance using Alpholio™’s patented methodology. For a detailed explanation of the methodology, please visit our FAQ. To see how the methodology is applied in practice, please review our blog posts that analyze various mutual funds.

To analyze a mutual fund, Alpholio™ finds a reference portfolio of exchange-traded funds (ETFs) that most closely tracks the fund’s performance over time. In general, there are three ways such a reference portfolio can be constructed with respect to its ETF membership and weights (percentages of each ETF’s value relative to the overall portfolio value):

Fixed membership and fixed weights (we call it a “regular fit”)

Fixed membership and variable weights (“fine fit”)

Variable membership and variable weights (“detailed fit”)

(The fourth alternative, variable membership and fixed weights, makes little sense.) More variability results in a more accurate fit, but it also causes more changes in the reference portfolio. A detailed fit, which selects ETFs from a large pool of candidates multiple times, is also much more computationally intensive than the other two.

To access the service, start the app, open the navigation drawer and tap the Mutual Fund item:

This will open a new screen, on which you can configure the analysis:

By default, the app analyzes FAIRX (The Fairholme Fund). To change the fund’s ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different fit type, tap the corresponding radio button. (The Detailed fit is disabled in the beta release of the app.)

After you specify all parameters, tap the Analyze Mutual Fund button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to use the service, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

This is a chart of the total returns of the analyzed fund and its reference ETF portfolios. To learn about the difference between the regular reference (Ref in the chart) and lag reference (Lag Ref) portfolios, please consult the FAQ.

To select a different analysis screen, tap the spinner on the action bar and then tap a corresponding item in the dropdown menu:

Here is the cumulative RealAlpha™ chart for FAIRX:

Below the chart, there is a Statistics section that you can collapse and expand by tapping on its header. The section contains annualized standard deviation for the fund and reference portfolio, as well as annualized discounted RealAlpha™ and RealBeta™ measures for the the regular and lag reference portfolios.

Here is the reference weights chart for FAIRX:

The Statistics section below the chart contains statics for each ETF in the reference portfolio. As you can see, the fund had a largest equivalent position in VFH (Vanguard Financials ETF). This was a reflection of large holdings in Fannie Mae and Freddie Mac, which recently caused the fund to lose 9.6% in a single day as a result of an unfavorable court ruling.

The Smooth Buy-Sell and EMA Buy-Sell charts provide hypothetical buy-sell signals derived from the cumulative RealAlpha™. The difference between the two stems from the smoothing method: The former uses forecasting (which has less latency but can cause more frequent buy-sell transitions), while the latter employs an exponential moving average (EMA) approach that has opposite characteristics. Here is a sample chart based on the first method:

Press or tap the Back button and change the fit type to Regular to see how this affects the membership and weights in the reference ETF portfolio for the fund. Give the app a try today:

]]>https://alpholio.com/blog/2014/10/24/alpholio-app-android-mutual-fund-service/feed/0Alpholio™ App for Android – Efficient Frontier Servicehttps://alpholio.com/blog/2014/10/23/alpholio-app-android-efficient-frontier-service/
https://alpholio.com/blog/2014/10/23/alpholio-app-android-efficient-frontier-service/#respondThu, 23 Oct 2014 23:03:44 +0000https://alpholio.com/blog/?p=1475Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the sixth one in a series covering the app’s services in more detail.

The Efficient Frontier service produces efficient frontier charts for a portfolio in the specified time frame. A full explanation of the efficient frontier and modern portfolio theory (MPT) is beyond the scope of this post. Among other places, you can find a good coverage of these concepts here and here.

To access the service, start the app, open the navigation drawer and tap the Efficient Frontier item:

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates and Return Frequency sections, simply tap each section header:

The Positions, Dates and Return Frequency sections are identical to their counterparts in the Portfolio service described in the previous post. However, settings for the Efficient Frontier service are separate from those of the Portfolio service.

After you specify all parameters, tap the Get Efficient Frontier button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

When the app obtains and processes the data, you should see the following screen:

The first thing you may notice is that the chart begins in October 2003 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003 and the first full month of returns for this ETF was the following month. The app automatically selected the largest possible date range for the analysis.

The efficient frontier (EF) is plotted in two sections: a small red one below the minimum-variance portfolio (MVP) and a large blue one above it. The capital allocation line (CAL) touches the upper EF section at the tangency portfolio (TP) point. Finally, the current portfolio (CP) is shown as a point inside the EF.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the – button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. The section contains precise expected-return / standard-deviation coordinates for the MVP, TP and CP. It also provides the risk-free rate and the maximum Sharpe ratio (that of the TP).

Press or tap the Back button on the device to change the weights of positions in the portfolio and see how the CP location changes with respect to the efficient frontier. Give the app a try today:

]]>https://alpholio.com/blog/2014/10/23/alpholio-app-android-efficient-frontier-service/feed/0Alpholio™ App for Android – Portfolio Servicehttps://alpholio.com/blog/2014/10/23/alpholio-app-android-portfolio-service/
https://alpholio.com/blog/2014/10/23/alpholio-app-android-portfolio-service/#respondThu, 23 Oct 2014 19:45:16 +0000https://alpholio.com/blog/?p=1466Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the fifth one in a series covering the app’s services in more detail.

The Portfolio service produces charts of total returns for portfolios composed of multiple securities and rebalanced with a specified frequency. (To better understand the importance of using total returns as opposed to price returns, please refer to the description of the app’s Total Return service.)

To access the service, start the app, open the navigation drawer and tap the Portfolio item:

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates, Return Frequency and Rebalance Frequency sections, simply tap each section header:

You can enter up to 20 portfolio positions by specifying a ticker and percentage weight for each. The weight determines the value of the position relative to the total value of the portfolio. For example, if the portfolio is worth $10,000 and a position has a weight of 25%, then the position’s value is $2,500. Position weights in a portfolio always add up to 100%.

To change a position’s ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To change a position weight, tap and drag the thumb of the corresponding seek bar until you see the desired percentage displayed above the bar. When you finish, weights of all other positions in the portfolio will automatically recalculate to add up to 100% (due to the seek bar resolution, there may be a rounding error of up to 1%). If you do not want the weight of a particular position to change, tap a corresponding Fix check box. If only one position remains unfixed, its weight cannot be changed.

To delete a position, tap its Del button; you will not be able to remove the last remaining position. When a position with non-zero weight is removed, its weight is distributed among the remaining positions according to their weights. To add a position, tap the Add Position button at the bottom of the list, then enter the new position’s ticker and set its weight.

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide a smoother return plot than weekly or daily ones.

To choose a rebalance frequency, expand the Rebalance Frequency section and tap the corresponding radio button:

Portfolio rebalancing involves adjusting positions to bring their weights to their original specification. The service assumes that trading costs are negligibly small compared to the position value. This is, for example, the case with no-transaction-fee ETFs at discount brokerages.

The frequency of rebalancing cannot be higher than the frequency of returns. For example, with monthly returns, portfolio can be rebalanced monthly, quarterly or semi-annually (i.e. every six months), but not daily or weekly. If you make both frequencies the same then the portfolio weights will effectively be kept constant (disregarding weight fluctuations in between rebalancing events).

After you specify all parameters, tap the Analyze Portfolio button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

When the app obtains and processes the data, you should see the following screen:

The first thing you may notice is that the chart begins in October 2003 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003 and the first full month of returns for this ETF was the following month. The app automatically selected the largest possible date range for the analysis.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the – button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. You can see that the portfolio had an annualized return of about 7.4% with an annualized standard deviation or returns of about 9.6%. The portfolio generated a modest amount of alpha but its beta was significantly lower than that of the market (by definition, equal to one). The Sharpe ratio of the portfolio was 0.64 and the maximum drawdown from the peak in October 2007 to the trough in March 2008 was about 34%.

Press or tap the Back button on your device and change some position weights or rebalancing frequency to see how that affects portfolio statistics. Give the app a try today:

]]>https://alpholio.com/blog/2014/10/23/alpholio-app-android-portfolio-service/feed/0Alpholio™ App for Android – Correlation Servicehttps://alpholio.com/blog/2014/10/23/alpholio-app-android-correlation-service/
https://alpholio.com/blog/2014/10/23/alpholio-app-android-correlation-service/#respondThu, 23 Oct 2014 15:40:53 +0000https://alpholio.com/blog/?p=1460Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the fourth one in a series covering the app’s services in more detail.

The Correlation service produces charts of the correlation coefficient between returns of two securities over a specified period. The correlation coefficient ranges from -1 (perfect negative correlation; returns always moving in opposite directions) to +1 (perfect positive correlation; returns always moving in same direction). A correlation coefficient of 0 indicates that returns of the two securities are unrelated or random with respect to each other. A correlation coefficient of close to +1 does not imply that the two securities are virtually identical (see our detailed explanation).

Knowing which securities are highly correlated and which are not is useful in portfolio construction. Generally, a security with a correlation coefficient of 0.6 or less in relation to others is considered a good candidate for portfolio diversification. Variations in the correlation coefficient over time are also important, especially in periods when one of the analyzed securities severely underperforms (see below).

To access the service, start the app, open the navigation drawer and tap the Correlation item:

This will open a new screen, on which you can enter inputs for the chart. To expand the Return Frequency and Span sections, simply tap each section header:

To change either ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog. The From date must chronologically precede the To date.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide a smoother correlation plot than weekly or daily ones.

To change the rolling correlation window, tap the Span field and use the pop-up keyboard to edit it. The span is expressed in the same units as the return frequency and has to be a whole number greater than one.

After you specify all parameters, tap the Get Correlation button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

The first thing you may notice is that the chart begins in October 2005 and not January 2000 that was specified as the From date. That is because the inception date of AGG was in September 2003, and two years worth of monthly returns were required to calculate the first data point. The app automatically selected the largest possible date range for the analysis.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the – button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. Here you can see that VTI and AGG returns were largely uncorrelated (both the mean and median correlation coefficient is very close to zero). However, at times this was not true, as indicated by the minimum and maximum values.

The forecast field projects the best estimate of correlation in the next time increment (in this example, October 2014). The estimate is calculated in a dynamic manner, using the entire data set of the chart.

In this example, you can see that the return correlation between the broad US equity and bond ETFs dramatically increased at the onset of the financial crisis in 2008 and did not subside until about three years later. One possible explanation: AGG is composed of Treasury, MBS and corporate investment-grade bonds, of which the last ones underperformed in 2008, similarly to their stock counterparts. After the equity market rebounded in early 2009, low interest rates caused a sustained positive correlation of bond returns with those of stocks.

We hope that you will find this service of help in finding and analyzing portfolio diversifiers. Give the app a try today:

]]>https://alpholio.com/blog/2014/10/23/alpholio-app-android-correlation-service/feed/0Alpholio™ App for Android – Rolling Returns Servicehttps://alpholio.com/blog/2014/10/22/alpholio-app-android-rolling-returns-service/
https://alpholio.com/blog/2014/10/22/alpholio-app-android-rolling-returns-service/#respondWed, 22 Oct 2014 19:40:32 +0000https://alpholio.com/blog/?p=1454Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the third one in a series covering the app’s services in more detail.

The Rolling Returns service compares returns of an analyzed security to those of a reference security in rolling intervals over a specified period. For example, consider a two-year period starting in January 2012 and ending in December 2013 with a rolling 12-month interval. The first comparison will be made in an interval from January 2012 through December 2012. Then the interval will move out (roll) by one month and span February 2012 through January 2013. The rolling will continue until the final interval covers January 2013 through December 2013. In total, there will be 13 comparisons between 12-month cumulative returns of the analyzed and reference security. Note that if the rolling interval has multiple time units (months in this example), successive intervals overlap in time.

Rolling returns are useful in determining the persistence of outperformance (or lack thereof) of an analyzed security vs. its reference. Unlike the typical one-, three-, five- and ten-year annualized returns, they are not anchored to a single point in time, frequently aligned to an artificial boundary of a calendar year. Instead, they cover various market conditions, especially those characterized by a high volatility. Finally, rolling returns more accurately reflect actual investment patterns.

To access the service, start the app, open the navigation drawer and tap the Rolling Returns item:

This will open a new screen, on which you can enter inputs for the chart. To expand the Dates, Return Frequency and Span sections, simply tap on each section header:

To change the analyzed or reference ticker, tap the corresponding field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.)

To modify either the From or To date, tap its corresponding button. This will pop up a standard date selection dialog.

To select a different return frequency, tap the corresponding radio button. Generally, monthly returns will provide smoother results than weekly or daily ones.

To change the rolling interval, tap on the Span field and use the pop-up keyboard to edit it. The span is expressed in the same units as the return frequency and has to be a positive whole number.

After you specify all parameters, tap the Get Rolling Returns button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be connected to the Internet.

After the app obtains and processes the data, you should see the following screen:

The first thing you may notice is that the bar chart begins in December 2005 and not December 2004 that was specified as the From date. That is because the first full 12-month rolling interval ends on the former date.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the – button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

Below the chart, there is a Statistics section that can be collapsed and expanded by tapping its header. The first part of the section contains statistics for rolling returns of the analyzed security. In this example, you can see that the average (mean) rolling return was lower than a median one, which indicates a left skew of the distribution. You can also see that the analyzed security had a very wide range of 12-month returns. Finally, you can see that the analyzed security returned more than the reference one in only 44% of all rolling intervals over the entire analysis period.

Scroll the Statistics section up to see the Difference part. As the name indicates, this part contains statistics for the differences of rolling returns. You can see that the median underperformance of the analyzed vs. reference security was 2% and that the differences of returns spanned quite a broad range.

We hope that this service will provide you with useful insights into performance of various investment vehicles. Give the app a try today:

]]>https://alpholio.com/blog/2014/10/22/alpholio-app-android-rolling-returns-service/feed/0Alpholio™ App for Android – Total Return Servicehttps://alpholio.com/blog/2014/10/21/alpholio-app-android-total-return-service/
https://alpholio.com/blog/2014/10/21/alpholio-app-android-total-return-service/#respondTue, 21 Oct 2014 19:40:22 +0000https://alpholio.com/blog/?p=1446Read more…]]>In one of the previous posts, we introduced the Alpholio™ app for Android. This post is the second one in a series covering the app’s services in more detail.

The Total Return service produces charts of total return for multiple securities over a common period. Unlike price returns, total returns account for dividends and other corporate actions of stocks. For mutual funds, this adjustment involves reinvestment of distributions.

To understand why this important, consider this finding from the “Dividend Choices” piece in the August 31, 2011 edition of the S&P The Outlook:

Over the long-term, dividends add significant value to stock. From 1926, dividends represent almost 42% of the total return of the S&P 500.

In that context, you may also want to review the following Wall Street Journal articles:

To modify a ticker, tap on its field and use the pop-up keyboard to edit it. (If you need to find the ticker based on other information, use the Security Lookup service of the app.) To delete a ticker, tap on the Del button on the same line; you will not be able to remove the last remaining ticker. To add a new ticker, tap the Add Ticker button at the bottom of the ticker list.

To change either the From or To date, tap its corresponding button. This will pop up a date selection dialog:

You can either scroll the month, day and year column, or tap on the respective item in the middle row to set it directly. Please note that the From date has to chronologically precede the To date.

Finally, you can select the frequency of returns to match the date range. Monthly returns (a default setting) are less volatile than weekly or daily ones, and will generally produce a smoother graph.

After you specify all parameters, tap the Get Total Return button. If any of your inputs are invalid, you will see a brief pop-up warning. If all settings are acceptable, they will be saved on the device for subsequent use. Please note that to generate the chart, your device must be able to access the Internet.

After the app obtains and processes the data, you should see the following screen:

The first thing you may notice is that the beginning date of the chart is May 30, 2003, and not January 1, 2000 that was specified as the From date in the previous screen. That is because the former date is the earliest possible one on which all three ETFs had a full-month return (the inception date of EEM is April 7, 2003). This is an example of how the app automatically adjusts input dates to provide sensible analysis outcomes in all services.

To zoom in on a portion of the chart, tap the + button or use a spread gesture. To scroll a zoomed-in chart horizontally or vertically, use a corresponding swipe gesture. To zoom out, tap the – button or use a pinch gesture. To immediately restore the chart to its original view, tap the 1:1 button.

The sample total return chart above shows that since mid-2003 the emerging market equities provided a much greater return than that of the developed market or domestic ones, but at the expense of higher volatility. Also, the cumulative return of domestic stocks recently exceeded that of developed market ones.

We hope that you will find this simple tool useful in further exploration of total returns. Give the app a try today: